Models and Measurement in Quantitative Sociology

The details
Colchester Campus
Full Year
Undergraduate: Level 6
Thursday 08 October 2020
Friday 02 July 2021
13 January 2021


Requisites for this module
SC203 or GV207 or SC208



Key module for

BSC L315 Sociology (Applied Quantitative Research),
BSC L316 Sociology (Applied Quantitative Research) (Including Year Abroad),
BSC L317 Sociology (Applied Quantitative Research) (Including Placement Year),
BSC L310 Sociology with Data Science,
BSC L311 Sociology with Data Science (including Year Abroad),
BSC L312 Sociology with Data Science (including Placement Year),
BSC L313 Sociology with Data Science (including foundation Year)

Module description

The first term of the module is focused on statistical models and begins with simple OLS regression and provides a framework for modelling strategy and variable selection. Students are then taken through extensions to the basic OLS model, with categorical predictors, interactions and non-linear terms. Next, we introduce models for categorical outcomes: binary logistic and multinomial logit. The term concludes with a discussion of practical topics in survey data analysis – how to deal with complex sample designs, weighting and non-response adjustments. The modelling framework outlined in this term builds the foundations for advanced quantitative social science methods.

The second term of the module introduces students to the data science concepts, techniques, and skills necessary to perform reproducible data analysis of variety of quantitative social data. Students will engage in hands-on reproducible data analysis workflow using open source computational tools, including the Python programming language, JupyterLab (and Jupyter Notebook), Markdown, GitHub, and the Open Science Framework (OSF). Prior knowledge of programming is not required and students that experience difficulties in installing software will have the opportunity to access it online from their laptops, tablets, or smartphones via JupyterHub. The students will learn, in an accessible way, basic models for machine learning, causal inference, and network analysis as well as practical data science skills, including data wrangling and visualization of various big data sources. The content is organized around three fundamental data science tasks--description (and exploratory data analysis), prediction, and causal inference (which includes experimental design). Attention is given to model evaluation and problems of selection bias, measurement error, confounding, and overfitting. Throughout the course are discussed issues of ethics, privacy, and fairness of quantitative models in social sciences.

Module aims

This module will develop students' understanding of quantitative analysis and impart the practical skills necessary for carrying out advanced statistical analysis of social data using modern statistical software.

Module learning outcomes

Transferable skills and learning outcomes

By the end of the module, you will be able to:
Perform, critically interpret, and communicate results from analysis using OLS regression, including models with categorical predictors, interactions, and non-linear terms.
• Perform, critically interpret, and communicate results from analysis using logistic and multinomial logit models.
• Deal with practical issues of data analysis, including complex sample designs, weighting and non-response adjustments.
• Freely and flexibly use computational tools—Python, Jupyter, Markdown—to perform reproducible data analysis and communicate your results.
• Wrangle, explore, visualize, and model your dataset using various Python libraries.
• Build an open and reproducible research workflow ranging from raw data to research report.
• Perform, critically interpret, and communicate results from analysis using basic models for machine learning, causal inference, and network analysis.
• Identify and deal with issues of selection bias, measurement error, confounding, and overfitting.
• Articulate and address issues of ethics, privacy, and fairness of quantitative models in the social domain.
• Write a clean, reusable code in Python.
• Use GitHub and OSF to share your work and collaborate on research projects with others.

Module information

If you wish to take this module but have not taken the second year module 'Researching Social Life II' (SC203-5-FY), please contact the module supervisor to see if you have the appropriate background in statistics.

Learning and teaching methods

No information available.


This module does not appear to have any essential texts. To see non-essential items, please refer to the module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework   Online Quiz    15% 
Coursework   Data Analysis Report  18/01/2021  35% 
Coursework   Data Analysis Exercise 1  26/02/2021  10% 
Coursework   Data Analysis Exercise 2  09/04/2021  10% 
Coursework   Data Analysis Report   21/05/2021  30% 

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Prof Nick Allum, email:
Dr Valentin Danchev, email:
Professor Nick Allum, Dr Valentin Danchev
Jane Harper, Undergraduate Administrator, Telephone: 01206 873052 E-mail:



External examiner

No external examiner information available for this module.
Available via Moodle
Of 2582 hours, 18 (0.7%) hours available to students:
2564 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information

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